At the root of the issue is a clash of belief systems.

Marketing thought is dominated by two rather large models of thinking. You have the Direct Paradigm and you have the Brand Paradigm. By Paradigm, I mean simply a way of looking at the world. Let me take one step back, and then one step forward.

People, in general, can only hold so many variables in their heads at the same time. So, we abstract. We’re supposed to derive some forms of causality that are important, throw that into some overarching architecture, and then use that framework to make decisions in a quick manner. When two people first approach a problem, and come at it from different paradigms, sometimes it can get nasty because there’s some questioning root assumptions.

The language you find in the Direct Paradigm is that the last action somebody took towards a sale is the most important. They point out, quite rightly, that repeated human behavior matters the most. A human in motion will tend to remain in motion. I wouldn’t make the accusation that all Direct people can only hold the short term in their minds at any given time. In fact, some of the best contributions and strongest predictors of campaign success are based on a very sophisticated understanding of time.

The language you find in the Brand Paradigm is that how somebody feels is the most important. They point out, quite rightly, that if somebody hates a brand, they won’t buy that brand. There’s a set of key performance indicators, invented in the early 1930′s to handle radio measurement, that attempt to quantify that. Likeability and message recall are the two big ones.

The Direct Paradigm tends to value deductive reasoning, and this is form and function as a result of having all the data. It’s inherently about data mining.

The Brand Paradigm though relies on inductive reasoning, because they’re forced to use sample statistics to perceive the world.

If you were to review what adherents of the Direct Paradigm are saying about sROI, they’re wagging their fingers and their tongues. They point out that Dell only made a fraction of their direct sales from Twitter. They point to the lack of conversion from the sales is proof positive that social media ROI is too low to justify intense spend.

If you were to review what adherents of the Brand Paradigm are saying about sROI, they’re clapping. They point out to successes like Best Buy and argue, quite rightly, that it impacts how people feel about a brand. They point to a fragmenting attention economy as being the main reason for intensifying social media spend.

So, which belief system – which paradigm – is right?

I’d argue that they’re both right.

I’ve read about an era in marketing when both the direct response and word of mouth dynamics were scientifically optimized. In my minds eye it was quite an exciting time.

I think we have to understand, fundamentally, that the Direct People have it right. They’re quite right that if you don’t have the right message to the right customer at the right time – you won’t get a sale. It’s about having the opportunity to convert being there at the right time.

I think the Brand people also have it right. How people feel about a brand is important. I’ll go so far as to say that how the friends of certain people feel about a brand, and how they consume that brand, is also a factor. Sure, you might put the right message to the right customer at the right time – but if I hate that company because they pulled their sponsorship for the Reading Rainbow…I’m not buying.

I’m optimistic that within social media measurement, through this quest for social media return on investment – that we’re going to find a satisfactory model that will be easy enough for 95% of the population to understand. That it’s going to incorporate just enough from the Direct Paradigm and just enough from the Branding Paradigm to work. In fact, what I’m seeing is a real opportunity for a Third Paradigm.

What if, under this Paradigm, we selected the most predictive elements, instead of what would be the easiest elements? What if, built into the model, we had a larger number of variables to chose from in constructing our general causal model? What if we acknowledged the dual nature social media?

I’ll add one factor that I think is especially salient: the nature of the product or service itself. There are certain products that are completely social in nature. It’s for this reason that I believe sROI is actually going to vary quite a bit depending on the sector and the competitive set.

The ultimate calculation will depend, quite heavily, on how much is borrowed from both Branding and Direct.

“Let’s say my shoe is loose. So I decide to bend down and tie it really tighter, inadvertently creating a knot. Let’s say the next morning, I have a hard time getting my shoe on – for let’s say, four minutes. Then let’s say that I miss my bus by just one minute. And the bus has a frequency of thirty minutes. Well then – one seemingly unrelated decision, made 16 hours before and taking all of 2 minutes to execute, has a 30 minute tardiness impact 16 hours later. That’s pretty much like the Butterfly Effect. Writ Small. And Mundane. Without bad acting.”

The Star Trek: TNG way of saying it would be “There’s a cascade failure in the warp core”. But enough of the Laforging.

Cause and Effect dynamics are devilish. After all, my lateness could have been chalked up to not being ten minutes early as I normally am. Or it could be chalked up to the bus being on time, which is unusual. I like to think of the world as a whole bunch of cones converging on a single point. Taken from this point of view, there are as many explanations for something happening as there are people. We all have our perception and are all entitled to own opinions. Though, we’re not entitled to our own facts. (wink).

It’s just a matter of which model has the greatest predictive strength. Normally I’d head down the rabbit hole into a bias about multiple regression…but no. This isn’t going to be a statistical rant. No. I have something far funner to read. (I hope).

And of what implications for the social systems we create?

Twitter is an excellent laboratory to study for that.

And that’s where we’re going to get into a lot of trouble with each other, as social media scientists.

‘How one seemingly innocuous tweet could cause a cascade failure in the warp core?’ will be one of those great analyses someday. And it will be contested. Loudly. By very educated and sinecure analysts.

It won’t necessarily because they won’t accept that little things can make such big impacts. I’ll be referring them on back to this post at that point. And surely, every very educated analyst should be familiar, and indeed, should have experienced such dynamics in their own lives so as to be able to relate. The Butterfly is in the Sky.

Rather, the debate might be how much causality to attribute to the originating tweet, and how much causality to attribute to the reinforcing effects. And indeed, this sub-branch of analytics, of reinforcement-attribution theory, is still very young in marketing science literature. (I salute those of you who have made contributions. It’s just that I wish we had a unified language to describe it.). Someday I’d like to be able to say: “Take a look. It’s in a book.”

How do we understand cause, intervening variables, and effect – and how much we decide to respect where each other is coming from, is by and large going to paint future debates. I’m optimistic that there will exist one school of social media measurement practitioners that will rely on evidence to make assessments. And I’d like to be in that school. I’m certain that we can go twice as high.

There was a little theme running throughout the post.

That’s how little things can make big impacts. And how something little will make something big.

Roger Martin, in “The Opposable Mind”, appears to de-emphasize the importance trade-offs.

Porter defines strategy is the process of making choices about activities that results in sustainable competitive advantage. Both books make reference to activity diagrams – so there’s unity and acknowledgement that choice matters. At the core: Porter explains the ‘why’ of strategic decision making, and Roger Martin describes the ‘how’ of strategic decision making. The ultimate way of showing trade-offs, in my view, is though the Production Possibility Frontier.

What is very elegant about the production possibility frontier (PPF) is that it’s two dimensional and tells a very clear story. There are trade offs between quality and quantity. Luxury and Economy. Bread and Guns. Reporting and Analysis. Accuracy and Precision. Very easy to understand.

Simplicity wins when you’re communicating. And yet, we’re rarely handed a manual when it comes to seeking elegant solutions.

Instead of just plotting two trade-offs and calling it a day, and instead of heading into n-space and Riemann spheres and unicorns and stickers – there is an elegant way of increasing salience while retaining understability.

If you use GGOBI or PASW, you can select ‘scatterplot matrix’ and you’ll get a n x n chart of all the activity-relationships. So, if you have 10 activities with tradeoffs and functions interrelating them, you’ll get a 100 charts, of which, 45 will be of use. (45 are just the mirror image, and 10 are straight line functions).

Through the magic of the post-it note, you can expand the number of activities under consideration quite a bit, and then start sorting them out on a very large wall.

What you might end up finding are a number of strategies that are very close any number of PPF curves (at least, the ones that matter). From there you can derive activity maps that correspond to any number of selected PPF curves. The elegant solution would be plot it against a single PPF curve, but I’m pessimistic (at this point) that such a solution would be common enough.

Instead of a single PPF, what if  it’s more of a Library of PPF’s?